Month: May 2017

As you may have read, Talend recently announced its support for Cloudera Altus, a newly released Platform-as-a-Service (PaaS) offering that simplifies running large-scale data processing applications in the public cloud. For us, supporting Altus at launch was the absolute easiest decision given that so many of our customers are looking to realize the cost, […]

This article originally appeared on Rilhia Solutions Many times during Data Integration projects we have situations where we have to analyze the data in order to come up with acceptance criteria for it. In a lot of cases, this is pretty straight forward and can be easily written into simple rule-based logic. But in some […]

While the whole world is shifting towards big data, NoSQL has become a crucial technology in the data management industry. The need for moving and transforming data between traditional and modern systems has likewise become mission critical for data-driven businesses. This data movement could either be to a new data warehouse project or migrating […]

In an earlier post on Applying Machine Learning to IoT Sensors, I discussed the process for classifying sensor data with a machine learning algorithm. In this post, I’ll give a background on choosing an algorithm, then using a validation technique. For the technique, I’ll show how to apply it, and how it can be built using the Talend […]

Talend has always been committed to open source technologies from our earliest days. As the years passed, we continued our track record of innovation in open source by committing ourselves to the latest Big Data technologies such as Spark, and Kafka. And now, with our redoubled commitment to cloud, it’s fitting that we talk about […]

In the first part of this discussion, we have addressed general challenges of IoT in Industry 4.0. In this second part, we will be outlining the key aspects of the concept of Industry 4.0 and touch on its connections to the complex aspects of data management. Germany’s Industry 4.0 and the Reference Architecture Model […]

Business Applications, Data Integration, Master Data Management, Data Warehousing, Big Data, Data Lakes, and Machine Learning; these all have (or should have) a common and essential ingredient: A Data Model; Let us NOT forget about that; Or, as in many situations I run into, ignore it completely! The Data Model is the backbone of almost all […]

Praying for next season will continue until the league gets better at data integration. Nobody loves stats and data more than football fans. From yards-after-catch (YAC) to possible correlations between the NFC winning the Super Bowl and a Republican winning the White House, rabid fans follow every conceivable story the numbers might tell. There […]

In this example, we have a set of XML files which represent a response to a stock update (fabricated example). The XML file has one critical field in it which is a text based description of the result of the transaction. We need to analyse the transaction results, and so need the data in […]